Welcome everyone to the next lecture of Introduction to Machine Learning.

I prepared again a little quiz for you.

You can participate if you go on this link, stutternfou.de, vote, and then 5 to y5 or

scanning the QR code.

Let's start.

First question is about Fourier series.

Maybe a hint, you can also do multiple choices.

That's not only a single choice question.

You need again the code?

You're welcome.

Eleven people voted.

We have quite some votes here already.

What do you think is correct?

I would say that looks quite good.

However, note that actually two answers are correct here.

The second and the fourth is correct.

You can apply the Fourier transform, of course, also on non-periodic functions.

You just go into the frequency domain.

Both is possible here.

Not only the last answer is correct.

Here the second and the fourth answer is correct.

Because Fourier series is only applicable to periodic signals, but the Fourier transform

extends it, the Fourier series, but it's also applicable to periodic functions.

Let's go to the next question.

The next question is, you have these figures in the time domain and you have the figures

here in the frequency domain.

Your question is to order them in the right, from top to bottom.

I should have numbered them instead of using letters.

What do you think belongs to what?

This graph, it's about the uncertainty relation between the frequency and the time domain.

Does it work for you?

You have to order them in the right order so that the letters fit basically, which are

here, are from top to bottom ordered.

Let's see.

Okay.

The most of you have it, right?

Okay.

Sorry.

Okay.

Let's maybe wait some more seconds.

Someone needs the QR code in the meantime.

Okay.

Good.

It seems the most of you have it right.

That's nice.

So I before, yeah, I think I can explain it a little bit.

So here, the narrower it is in the time domain, the wider it is in the frequency domain.

So this peak here would belong to the farthest, so to the most widest function in the frequency

domain and of course vice versa.

So it goes here from very peaky to very wide, so very narrow function to a wide function